Update lint

.github/workflows/isort.yml

@@ -0,0 +1,24 @@
+name: Run isort
+
+on:
+  push:
+  pull_request:
+  merge_group:
+
+jobs:
+  isort:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Install poetry
+        run: pipx install poetry
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          poetry install --with dev
+      - name: Check code with isort
+        run: |
+          poetry run isort . -c

.pre-commit-config.yaml

@@ -0,0 +1,20 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+-   repo: local
+    hooks:
+    -   id: black
+        name: black
+        entry: black
+        language: system
+        types: [ python ]
+-   repo: local
+    hooks:
+      - id: isort
+        name: isort (python)
+        entry: isort
+        language: system
+        types: [ python ]

README.md

@@ -73,6 +73,12 @@ pip install -e ".[dev]"
 Either way will give you the very latest copy of the code, update the installed version if you git pull or modify scripts 
 yourself, and still enable you to import flarestack.
 
+Finally, install the pre-commit hooks:
+
+```shell
+pre-commit install
+```
+
 If you do want to contribute to _flarestack_, you can check out some guidelines [here](https://github.com/icecube/flarestack/blob/master/CONTRIBUTING.md).
 
 

examples/create_scrambled_dataset.py

@@ -1,7 +1,8 @@
-from flarestack.data.icecube import gfu_v002_p04
-from flarestack.utils import ps_catalogue_name, load_catalogue
 import logging
 
+from flarestack.data.icecube import gfu_v002_p04
+from flarestack.utils import load_catalogue, ps_catalogue_name
+
 logging.getLogger().setLevel("INFO")
 
 # Initialise Injectors/LLHs

flarestack/__init__.py

@@ -1,6 +1,6 @@
-from flarestack.core.minimisation import MinimisationHandler
-from flarestack.core.results import ResultsHandler, OverfluctuationError
 from flarestack.cluster import analyse, wait_cluster
-from flarestack.core.unblinding import create_unblinder
 from flarestack.core.energy_pdf import EnergyPDF
+from flarestack.core.minimisation import MinimisationHandler
+from flarestack.core.results import OverfluctuationError, ResultsHandler
+from flarestack.core.unblinding import create_unblinder
 from flarestack.shared import *

flarestack/analyses/agn_cores/create_catalogue.py

@@ -7,23 +7,24 @@
 remaining 30% weight distributed across many sources. As integer numbers of
 sources are used, the closest percentage to 70% is used for splitting.
 """
-from __future__ import print_function
-from __future__ import absolute_import
+
+from __future__ import absolute_import, print_function
+
+import os
 from builtins import zip
+
+import astropy.io.fits as pyfits
+import numpy as np
+from astropy.table import Table
+
 from flarestack.analyses.agn_cores.shared_agncores import (
-    raw_cat_dir,
     agn_catalogue_name,
     agn_cores_output_dir,
-)
-from flarestack.analyses.agn_cores.shared_agncores import (
     create_random_src,
     plot_catalogue,
+    raw_cat_dir,
 )
 from flarestack.utils.prepare_catalogue import cat_dtype
-import astropy.io.fits as pyfits
-from astropy.table import Table
-import numpy as np
-import os
 
 
 def select_nrandom_sources(cat, n_random=100):

flarestack/analyses/agn_cores/diffuse_dataset/test_completeness_diffuse_8years_cluster_collectdata.py

@@ -3,47 +3,45 @@
 Sensitivities are given as the total integrated fluence across all sources,
 and as the corresponding standard-candle-luminosity.
 """
-from __future__ import print_function
-from __future__ import division
+
+from __future__ import division, print_function
+
+import logging
+import math
+import os
+import resource
+import time
+
+import matplotlib.pyplot as plt
 import numpy as np
+import psutil  # to get memory usage info
+from matplotlib.ticker import ScalarFormatter
+
+from flarestack.analyses.agn_cores.shared_agncores import (
+    agn_catalogue_name,
+    agn_subset_catalogue,
+    agn_subset_catalogue_north,
+    complete_cats,
+)
+from flarestack.cluster import analyse, wait_for_cluster
+from flarestack.core.minimisation import MinimisationHandler
 from flarestack.core.results import ResultsHandler
+from flarestack.data.icecube import diffuse_8_year
 
 # # from flarestack.data.icecube.ps_tracks.ps_v002_p01 import ps_7year
 # from flarestack.data.icecube.ps_tracks.ps_v003_p02 import ps_10year
 # from flarestack.data.icecube.northern_tracks.nt_v002_p05 import diffuse_8year
 # from flarestack.data.icecube.gfu.gfu_v002_p01 import txs_sample_v1
 from flarestack.shared import (
-    plot_output_dir,
     flux_to_k,
-    make_analysis_pickle,
     k_to_flux,
+    make_analysis_pickle,
+    plot_output_dir,
 )
-
-from flarestack.data.icecube import diffuse_8_year
 from flarestack.utils.catalogue_loader import load_catalogue
-from flarestack.analyses.agn_cores.shared_agncores import (
-    agn_subset_catalogue,
-    complete_cats,
-    agn_catalogue_name,
-    agn_subset_catalogue_north,
-)
-from flarestack.core.minimisation import MinimisationHandler
-
-from flarestack.cluster import analyse, wait_for_cluster
-import math
-import matplotlib.pyplot as plt
-from matplotlib.ticker import ScalarFormatter
-
-import matplotlib.pyplot as plt
 
 # plt.style.use('~/scratch/phdthesis.mpltstyle')
 
-import time
-
-import logging
-
-import os
-import psutil, resource  # to get memory usage info
 
 analyses = dict()
 
@@ -691,9 +689,8 @@ def generate_name(cat_key, n_sources, gamma):
             frame2.set_ylabel("Stacked\nsources")
             frame2.set_yticks([10, 1000, 10000])
 
-            from matplotlib.ticker import AutoMinorLocator
-            from matplotlib.ticker import StrMethodFormatter
             from matplotlib import ticker
+            from matplotlib.ticker import AutoMinorLocator, StrMethodFormatter
 
             minorLocator = AutoMinorLocator()
             # frame2.xaxis.set_minor_locator(minorLocator)
@@ -819,9 +816,8 @@ def generate_name(cat_key, n_sources, gamma):
         frame2.set_yticks([10, 1000, 10000])
         frame2.set_ylabel("Stacked\nsources")
 
-        from matplotlib.ticker import AutoMinorLocator
-        from matplotlib.ticker import StrMethodFormatter
         from matplotlib import ticker
+        from matplotlib.ticker import AutoMinorLocator, StrMethodFormatter
 
         minorLocator = AutoMinorLocator()
         # frame2.xaxis.set_minor_locator(minorLocator)

flarestack/analyses/agn_cores/loop_over_catalogues.py

@@ -3,17 +3,20 @@
 Sensitivities are given as the total integrated fluence across all sources,
 and as the corresponding standard-candle-luminosity.
 """
+
+import math
 from builtins import str
+
+import matplotlib.pyplot as plt
 import numpy as np
+
+from flarestack.analyses.tde.shared_TDE import tde_catalogue_name, tde_catalogues
+from flarestack.cluster import run_desy_cluster as rd
 from flarestack.core.results import ResultsHandler
 from flarestack.data.icecube.gfu.gfu_v002_p01 import txs_sample_v1
-from flarestack.shared import plot_output_dir, flux_to_k, make_analysis_pickle
-from flarestack.utils.reference_sensitivity import reference_sensitivity
-from flarestack.analyses.tde.shared_TDE import tde_catalogues, tde_catalogue_name
-from flarestack.cluster import run_desy_cluster as rd
-import math
-import matplotlib.pyplot as plt
+from flarestack.shared import flux_to_k, make_analysis_pickle, plot_output_dir
 from flarestack.utils.custom_dataset import custom_dataset
+from flarestack.utils.reference_sensitivity import reference_sensitivity
 
 analyses = dict()
 

flarestack/analyses/agn_cores/scripts_for_unblinding/agn_catalogues/create_ir_selected_agn_north_catalogue.py

@@ -5,18 +5,21 @@
 and XMMSL2 and NVSS. From the two subsamples 3LAC blazars have been removed.
 Also double X-Ray sources and double radio sources have been removed.
 """
+
+import os
+
+import astropy.io.fits as pyfits
+import numpy as np
+import pandas as pd
+from astropy.table import Table
+from shared_agncores import create_random_src, plot_catalogue
+
 from flarestack.analyses.agn_cores.shared_agncores import (
-    raw_cat_dir,
     agn_catalogue_name,
     agn_cores_output_dir,
+    raw_cat_dir,
 )
-from shared_agncores import create_random_src, plot_catalogue
 from flarestack.utils.prepare_catalogue import cat_dtype
-import astropy.io.fits as pyfits
-from astropy.table import Table
-import numpy as np
-import pandas as pd
-import os
 
 
 def select_nrandom_sources(cat, n_random=100):

flarestack/analyses/agn_cores/scripts_for_unblinding/agn_catalogues/create_llagn_north_catalogue.py

@@ -5,18 +5,21 @@
 and XMMSL2 and NVSS. From the two subsamples 3LAC blazars have been removed.
 Also double X-Ray sources and double radio sources have been removed.
 """
+
+import os
+
+import astropy.io.fits as pyfits
+import numpy as np
+import pandas as pd
+from astropy.table import Table
+from shared_agncores import create_random_src, plot_catalogue
+
 from flarestack.analyses.agn_cores.shared_agncores import (
-    raw_cat_dir,
     agn_catalogue_name,
     agn_cores_output_dir,
+    raw_cat_dir,
 )
-from shared_agncores import create_random_src, plot_catalogue
 from flarestack.utils.prepare_catalogue import cat_dtype
-import astropy.io.fits as pyfits
-from astropy.table import Table
-import numpy as np
-import pandas as pd
-import os
 
 
 def select_nrandom_sources(cat, n_random=100):

flarestack/analyses/agn_cores/scripts_for_unblinding/agn_catalogues/create_radio_selected_agn_north_catalogue.py

@@ -5,18 +5,21 @@
 and XMMSL2 and NVSS. From the two subsamples 3LAC blazars have been removed.
 Also double X-Ray sources and double radio sources have been removed.
 """
+
+import os
+
+import astropy.io.fits as pyfits
+import numpy as np
+import pandas as pd
+from astropy.table import Table
+from shared_agncores import create_random_src, plot_catalogue
+
 from flarestack.analyses.agn_cores.shared_agncores import (
-    raw_cat_dir,
     agn_catalogue_name,
     agn_cores_output_dir,
+    raw_cat_dir,
 )
-from shared_agncores import create_random_src, plot_catalogue
 from flarestack.utils.prepare_catalogue import cat_dtype
-import astropy.io.fits as pyfits
-from astropy.table import Table
-import numpy as np
-import pandas as pd
-import os
 
 
 def select_nrandom_sources(cat, n_random=100):

flarestack/analyses/agn_cores/scripts_for_unblinding/create_agn_samples/create_ir_selected_agn_north_catalogue.py

@@ -5,18 +5,21 @@
 and XMMSL2 and NVSS. From the two subsamples 3LAC blazars have been removed.
 Also double X-Ray sources and double radio sources have been removed.
 """
+
+import os
+
+import astropy.io.fits as pyfits
+import numpy as np
+import pandas as pd
+from astropy.table import Table
+from shared_agncores import create_random_src, plot_catalogue
+
 from flarestack.analyses.agn_cores.shared_agncores import (
-    raw_cat_dir,
     agn_catalogue_name,
     agn_cores_output_dir,
+    raw_cat_dir,
 )
-from shared_agncores import create_random_src, plot_catalogue
 from flarestack.utils.prepare_catalogue import cat_dtype
-import astropy.io.fits as pyfits
-from astropy.table import Table
-import numpy as np
-import pandas as pd
-import os
 
 
 def select_nrandom_sources(cat, n_random=100):

flarestack/analyses/agn_cores/scripts_for_unblinding/create_agn_samples/create_llagn_north_catalogue.py

@@ -5,18 +5,21 @@
 and removing the 3LAC blazars. A Seyferntess PDF is assigned and only sources with
 Seyfertness larger than 0.5 are selected in the final sample.
 """
+
+import os
+
+import astropy.io.fits as pyfits
+import numpy as np
+import pandas as pd
+from astropy.table import Table
+from shared_agncores import create_random_src, plot_catalogue
+
 from flarestack.analyses.agn_cores.shared_agncores import (
-    raw_cat_dir,
     agn_catalogue_name,
     agn_cores_output_dir,
+    raw_cat_dir,
 )
-from shared_agncores import create_random_src, plot_catalogue
 from flarestack.utils.prepare_catalogue import cat_dtype
-import astropy.io.fits as pyfits
-from astropy.table import Table
-import numpy as np
-import pandas as pd
-import os
 
 
 def select_nrandom_sources(cat, n_random=100):